Redis data sharding – part 1

In one of my previous posts on Redis i provided a definition of data sharding, quoting a great book “Redis in Action” authored by Dr. Josiah L Carlson:

  • “Sharding is a method by which you partition your data into different pieces. In this case, you partition your data based on IDs embedded in the keys, based on the hash of keys, or some combination of the two. Through partitioning your data, you can store and fetch the data from multiple machines, which can allow a linear scaling in performance for certain problem domains.”

 

Today i’d like to elaborate some more on data sharding based on IDs embedded in the keys.

 

Let’s start with an example of a hypothetical data stored in an Redis instance:

redis 127.0.0.1:6379>keys *
(empty list or set)
redis 127.0.0.1:6379>set emails:1 me@mariuszprzydatek.com
OK
redis 127.0.0.1:6379>get emails:1
"me@mariuszprzydatek.com"

what i did here is to use the basic String data type to store an email of a user. As you can see, i embedded user id within the key (’emails:1′). Now, if a front-end application would ask for an email address of a user with id=1, on the back-end side i would concatenate the keyword which i usually use to denote keys i store emails under (ie. ’emails’) with the id of a user (‘1’), add a colon (‘:’) in between, and this way i’ll get the resulting key (’emails:1′) i should look after while making a call to the Redis instance.

 

This solution is nice but if i’ll have 1 million of users registered in my system and use Redis as the data store for keeping mappings between identifier of a user and his email, i will end up with 1 million keys (’emails:1′, ’emails:2′, ’emails:3′, etc.). This is a volume my Redis instance will easily handle (see my previous post on Redis Performance Basics) and it will use little more than 190MB to store everything in the memory (so much due to a lot of overhead when storing small keys and values; the ratio is much better with large keys/values), but this is only one attribute we’re talking about – and what about firstName, lastName, etc.?. Obviously, if my system will have millions of registered users and i’d use Redis as my primary data store for users-related info, i would be running multiple instances of Redis already and based on the id of a user, route the queries to a specific instance, but there’s still a lot we can do to optimize costs prior to thinking about scaling.

 

Small code snippet to generate 1M of emails stored in Redis using String data structure (and Spring Data Redis mentioned in my other post).

int i = 0;
while(i<1000000) {
    redisTemplate.opsForValue().set(String.format("emails:%s", i++), "me@mariuszprzydatek.com");
}

the loop above executes in 2 mins on my Windows 8 64bit i7 laptop and the ‘redis-server’ process allocates ca 190 MB of memory.

 

Now, what will happen if we change the data structure let’s say to a Redis Hash?

Next code snippet and we’re getting just that:

int i = 0;
while(i<1000000) {
    String userId = String.valueOf(i++);
    String emailAddress = String.format("user_%s@mariuszprzydatek.com", userId);
    redisTemplate.opsForHash().put("emails", emailAddress, userId)
}

2 mins and 165 MB of memory allocated – a 15 % gain absolutely for free.

 

Let’s try with data sharding/partitioning. Another code snippet using Redis Hash data structure and there you go:

int shardSize = 1024;
int i = 0;
while(i<1000000) {
    int shardKey = i/shardSize;
    String userId = String.valueOf(i++);
    String emailAddress = String.format("user_%s@mariuszprzydatek.com", userId);
    redisTemplate.opsForHash().put(String.format("emailsbucket:%s", shardKey), emailAddress, userId);
}

2 mins later and… only 30 MB allocated – now you’re talking Mariusz!

Staggering 530 % increase in memory allocation efficiency!

 

Hope you enjoyed the first part of this brief tutorial.

 

Cheers!

 

 

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